Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations2126
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)0.5%
Total size in memory365.5 KiB
Average record size in memory176.1 B

Variable types

Numeric19
Categorical3

Alerts

Dataset has 11 (0.5%) duplicate rowsDuplicates
abnormal_short_term_variability is highly overall correlated with mean_value_of_short_term_variabilityHigh correlation
baseline value is highly overall correlated with histogram_mean and 2 other fieldsHigh correlation
histogram_max is highly overall correlated with histogram_number_of_peaks and 2 other fieldsHigh correlation
histogram_mean is highly overall correlated with baseline value and 2 other fieldsHigh correlation
histogram_median is highly overall correlated with baseline value and 2 other fieldsHigh correlation
histogram_min is highly overall correlated with histogram_number_of_peaks and 4 other fieldsHigh correlation
histogram_mode is highly overall correlated with baseline value and 3 other fieldsHigh correlation
histogram_number_of_peaks is highly overall correlated with histogram_max and 4 other fieldsHigh correlation
histogram_variance is highly overall correlated with histogram_max and 6 other fieldsHigh correlation
histogram_width is highly overall correlated with histogram_max and 6 other fieldsHigh correlation
light_decelerations is highly overall correlated with histogram_min and 3 other fieldsHigh correlation
mean_value_of_short_term_variability is highly overall correlated with abnormal_short_term_variability and 6 other fieldsHigh correlation
percentage_of_time_with_abnormal_long_term_variability is highly overall correlated with histogram_variance and 2 other fieldsHigh correlation
severe_decelerations is highly overall correlated with histogram_modeHigh correlation
severe_decelerations is highly imbalanced (96.8%) Imbalance
accelerations has 894 (42.1%) zeros Zeros
fetal_movement has 1311 (61.7%) zeros Zeros
uterine_contractions has 332 (15.6%) zeros Zeros
light_decelerations has 1231 (57.9%) zeros Zeros
prolongued_decelerations has 1948 (91.6%) zeros Zeros
percentage_of_time_with_abnormal_long_term_variability has 1240 (58.3%) zeros Zeros
mean_value_of_long_term_variability has 137 (6.4%) zeros Zeros
histogram_number_of_peaks has 107 (5.0%) zeros Zeros
histogram_number_of_zeroes has 1624 (76.4%) zeros Zeros
histogram_variance has 187 (8.8%) zeros Zeros

Reproduction

Analysis started2025-03-08 14:12:38.230471
Analysis finished2025-03-08 14:14:35.041578
Duration1 minute and 56.81 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

baseline value
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.30386
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:35.434854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8408443
Coefficient of variation (CV)0.073822652
Kurtosis-0.29294291
Mean133.30386
Median Absolute Deviation (MAD)7
Skewness0.020312189
Sum283404
Variance96.842216
MonotonicityNot monotonic
2025-03-08T11:14:35.857972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 136
 
6.4%
130 111
 
5.2%
122 109
 
5.1%
138 103
 
4.8%
125 91
 
4.3%
128 85
 
4.0%
120 78
 
3.7%
142 77
 
3.6%
144 77
 
3.6%
132 76
 
3.6%
Other values (38) 1183
55.6%
ValueCountFrequency (%)
106 7
 
0.3%
110 21
 
1.0%
112 16
 
0.8%
114 11
 
0.5%
115 28
 
1.3%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 17
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 12
0.6%
158 10
 
0.5%
157 4
 
0.2%
156 4
 
0.2%
154 8
 
0.4%
152 17
0.8%
151 14
0.7%
150 26
1.2%
149 18
0.8%

accelerations
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003178269
Minimum0
Maximum0.019
Zeros894
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:36.217193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002
Q30.006
95-th percentile0.011
Maximum0.019
Range0.019
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.003865591
Coefficient of variation (CV)1.2162567
Kurtosis0.76764826
Mean0.003178269
Median Absolute Deviation (MAD)0.002
Skewness1.2043921
Sum6.757
Variance1.4942793 × 10-5
MonotonicityNot monotonic
2025-03-08T11:14:36.556274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 894
42.1%
0.003 161
 
7.6%
0.002 160
 
7.5%
0.001 143
 
6.7%
0.004 117
 
5.5%
0.006 112
 
5.3%
0.005 110
 
5.2%
0.008 103
 
4.8%
0.007 91
 
4.3%
0.009 60
 
2.8%
Other values (10) 175
 
8.2%
ValueCountFrequency (%)
0 894
42.1%
0.001 143
 
6.7%
0.002 160
 
7.5%
0.003 161
 
7.6%
0.004 117
 
5.5%
0.005 110
 
5.2%
0.006 112
 
5.3%
0.007 91
 
4.3%
0.008 103
 
4.8%
0.009 60
 
2.8%
ValueCountFrequency (%)
0.019 1
 
< 0.1%
0.018 2
 
0.1%
0.017 4
 
0.2%
0.016 7
 
0.3%
0.015 9
 
0.4%
0.014 20
 
0.9%
0.013 22
1.0%
0.012 24
1.1%
0.011 36
1.7%
0.01 50
2.4%

fetal_movement
Real number (ℝ)

Zeros 

Distinct102
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009480715
Minimum0
Maximum0.481
Zeros1311
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:36.954162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.028
Maximum0.481
Range0.481
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.046665844
Coefficient of variation (CV)4.9221862
Kurtosis64.260821
Mean0.009480715
Median Absolute Deviation (MAD)0
Skewness7.8114772
Sum20.156
Variance0.002177701
MonotonicityNot monotonic
2025-03-08T11:14:37.391188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1311
61.7%
0.001 164
 
7.7%
0.002 112
 
5.3%
0.003 88
 
4.1%
0.004 49
 
2.3%
0.005 36
 
1.7%
0.006 31
 
1.5%
0.007 28
 
1.3%
0.008 25
 
1.2%
0.01 25
 
1.2%
Other values (92) 257
 
12.1%
ValueCountFrequency (%)
0 1311
61.7%
0.001 164
 
7.7%
0.002 112
 
5.3%
0.003 88
 
4.1%
0.004 49
 
2.3%
0.005 36
 
1.7%
0.006 31
 
1.5%
0.007 28
 
1.3%
0.008 25
 
1.2%
0.009 25
 
1.2%
ValueCountFrequency (%)
0.481 1
< 0.1%
0.477 1
< 0.1%
0.47 1
< 0.1%
0.469 1
< 0.1%
0.455 1
< 0.1%
0.451 1
< 0.1%
0.446 1
< 0.1%
0.443 1
< 0.1%
0.441 1
< 0.1%
0.43 1
< 0.1%

uterine_contractions
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0043664158
Minimum0
Maximum0.015
Zeros332
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:37.763155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.004
Q30.007
95-th percentile0.009
Maximum0.015
Range0.015
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.0029460691
Coefficient of variation (CV)0.67471108
Kurtosis-0.63507123
Mean0.0043664158
Median Absolute Deviation (MAD)0.002
Skewness0.15931455
Sum9.283
Variance8.6793233 × 10-6
MonotonicityNot monotonic
2025-03-08T11:14:38.104363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 332
15.6%
0.005 290
13.6%
0.004 244
11.5%
0.006 231
10.9%
0.007 216
10.2%
0.003 212
10.0%
0.008 160
7.5%
0.002 160
7.5%
0.001 118
 
5.6%
0.009 82
 
3.9%
Other values (6) 81
 
3.8%
ValueCountFrequency (%)
0 332
15.6%
0.001 118
 
5.6%
0.002 160
7.5%
0.003 212
10.0%
0.004 244
11.5%
0.005 290
13.6%
0.006 231
10.9%
0.007 216
10.2%
0.008 160
7.5%
0.009 82
 
3.9%
ValueCountFrequency (%)
0.015 1
 
< 0.1%
0.014 2
 
0.1%
0.013 2
 
0.1%
0.012 11
 
0.5%
0.011 16
 
0.8%
0.01 49
 
2.3%
0.009 82
 
3.9%
0.008 160
7.5%
0.007 216
10.2%
0.006 231
10.9%

light_decelerations
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0018894638
Minimum0
Maximum0.015
Zeros1231
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:38.441707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.008
Maximum0.015
Range0.015
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0029602086
Coefficient of variation (CV)1.5666924
Kurtosis2.5174609
Mean0.0018894638
Median Absolute Deviation (MAD)0
Skewness1.7184369
Sum4.017
Variance8.7628348 × 10-6
MonotonicityNot monotonic
2025-03-08T11:14:39.152248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1231
57.9%
0.001 163
 
7.7%
0.003 118
 
5.6%
0.002 115
 
5.4%
0.004 114
 
5.4%
0.005 107
 
5.0%
0.006 74
 
3.5%
0.008 55
 
2.6%
0.007 54
 
2.5%
0.009 37
 
1.7%
Other values (6) 58
 
2.7%
ValueCountFrequency (%)
0 1231
57.9%
0.001 163
 
7.7%
0.002 115
 
5.4%
0.003 118
 
5.6%
0.004 114
 
5.4%
0.005 107
 
5.0%
0.006 74
 
3.5%
0.007 54
 
2.5%
0.008 55
 
2.6%
0.009 37
 
1.7%
ValueCountFrequency (%)
0.015 3
 
0.1%
0.014 7
 
0.3%
0.013 8
 
0.4%
0.012 12
 
0.6%
0.011 13
 
0.6%
0.01 15
 
0.7%
0.009 37
1.7%
0.008 55
2.6%
0.007 54
2.5%
0.006 74
3.5%

severe_decelerations
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.7 KiB
0.0
2119 
0.001
 
7

Length

Max length5
Median length3
Mean length3.0065851
Min length3

Characters and Unicode

Total characters6392
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2119
99.7%
0.001 7
 
0.3%

Length

2025-03-08T11:14:39.542115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-08T11:14:39.890839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2119
99.7%
0.001 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

prolongued_decelerations
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00015851364
Minimum0
Maximum0.005
Zeros1948
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:40.164979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.002
Maximum0.005
Range0.005
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00058994752
Coefficient of variation (CV)3.7217461
Kurtosis20.515918
Mean0.00015851364
Median Absolute Deviation (MAD)0
Skewness4.3239651
Sum0.337
Variance3.4803807 × 10-7
MonotonicityNot monotonic
2025-03-08T11:14:40.683085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1948
91.6%
0.002 72
 
3.4%
0.001 70
 
3.3%
0.003 24
 
1.1%
0.004 9
 
0.4%
0.005 3
 
0.1%
ValueCountFrequency (%)
0 1948
91.6%
0.001 70
 
3.3%
0.002 72
 
3.4%
0.003 24
 
1.1%
0.004 9
 
0.4%
0.005 3
 
0.1%
ValueCountFrequency (%)
0.005 3
 
0.1%
0.004 9
 
0.4%
0.003 24
 
1.1%
0.002 72
 
3.4%
0.001 70
 
3.3%
0 1948
91.6%

abnormal_short_term_variability
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.990122
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:41.037289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.192814
Coefficient of variation (CV)0.36588144
Kurtosis-1.0510296
Mean46.990122
Median Absolute Deviation (MAD)14
Skewness-0.011828576
Sum99901
Variance295.59284
MonotonicityNot monotonic
2025-03-08T11:14:41.469229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 62
 
2.9%
58 61
 
2.9%
65 60
 
2.8%
63 58
 
2.7%
64 58
 
2.7%
61 57
 
2.7%
51 54
 
2.5%
62 51
 
2.4%
22 48
 
2.3%
25 46
 
2.2%
Other values (65) 1571
73.9%
ValueCountFrequency (%)
12 2
 
0.1%
13 7
 
0.3%
14 4
 
0.2%
15 4
 
0.2%
16 12
 
0.6%
17 13
 
0.6%
18 10
 
0.5%
19 19
0.9%
20 27
1.3%
21 33
1.6%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 4
 
0.2%
84 6
 
0.3%
83 4
 
0.2%
82 2
 
0.1%
81 7
 
0.3%
80 7
 
0.3%
79 15
0.7%
78 19
0.9%
77 16
0.8%

mean_value_of_short_term_variability
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3327846
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:41.886071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88324133
Coefficient of variation (CV)0.66270375
Kurtosis4.7007563
Mean1.3327846
Median Absolute Deviation (MAD)0.5
Skewness1.6573392
Sum2833.5
Variance0.78011525
MonotonicityNot monotonic
2025-03-08T11:14:42.296954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 125
 
5.9%
1.3 121
 
5.7%
0.5 121
 
5.7%
0.4 120
 
5.6%
0.7 117
 
5.5%
0.9 114
 
5.4%
0.6 113
 
5.3%
1.2 107
 
5.0%
1.5 100
 
4.7%
1 99
 
4.7%
Other values (47) 989
46.5%
ValueCountFrequency (%)
0.2 47
 
2.2%
0.3 84
4.0%
0.4 120
5.6%
0.5 121
5.7%
0.6 113
5.3%
0.7 117
5.5%
0.8 125
5.9%
0.9 114
5.4%
1 99
4.7%
1.1 97
4.6%
ValueCountFrequency (%)
7 1
< 0.1%
6.9 1
< 0.1%
6.3 2
0.1%
6 1
< 0.1%
5.9 1
< 0.1%
5.7 1
< 0.1%
5.4 2
0.1%
5.3 1
< 0.1%
5.2 1
< 0.1%
5 2
0.1%

percentage_of_time_with_abnormal_long_term_variability
Real number (ℝ)

High correlation  Zeros 

Distinct87
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8466604
Minimum0
Maximum91
Zeros1240
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:42.702506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.39688
Coefficient of variation (CV)1.868337
Kurtosis4.2529979
Mean9.8466604
Median Absolute Deviation (MAD)0
Skewness2.1950753
Sum20934
Variance338.44518
MonotonicityNot monotonic
2025-03-08T11:14:43.290557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
5 43
 
2.0%
4 40
 
1.9%
3 36
 
1.7%
8 34
 
1.6%
6 31
 
1.5%
12 29
 
1.4%
10 23
 
1.1%
Other values (77) 553
26.0%
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
3 36
 
1.7%
4 40
 
1.9%
5 43
 
2.0%
6 31
 
1.5%
7 23
 
1.1%
8 34
 
1.6%
9 22
 
1.0%
ValueCountFrequency (%)
91 4
0.2%
90 2
 
0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%
84 6
0.3%
82 1
 
< 0.1%
81 2
 
0.1%
79 1
 
< 0.1%
78 3
0.1%

mean_value_of_long_term_variability
Real number (ℝ)

Zeros 

Distinct249
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1876294
Minimum0
Maximum50.7
Zeros137
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:43.689776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.475
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.6282466
Coefficient of variation (CV)0.68740857
Kurtosis4.1312538
Mean8.1876294
Median Absolute Deviation (MAD)3.1
Skewness1.3319979
Sum17406.9
Variance31.67716
MonotonicityNot monotonic
2025-03-08T11:14:44.209336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
6.4%
7.1 29
 
1.4%
6.7 29
 
1.4%
6.5 25
 
1.2%
5.2 25
 
1.2%
9.5 24
 
1.1%
6.8 23
 
1.1%
5.6 23
 
1.1%
7.2 23
 
1.1%
8.5 23
 
1.1%
Other values (239) 1765
83.0%
ValueCountFrequency (%)
0 137
6.4%
0.1 4
 
0.2%
0.2 4
 
0.2%
0.3 9
 
0.4%
0.4 6
 
0.3%
0.5 11
 
0.5%
0.6 3
 
0.1%
0.7 4
 
0.2%
0.8 1
 
< 0.1%
0.9 5
 
0.2%
ValueCountFrequency (%)
50.7 1
< 0.1%
41.8 1
< 0.1%
40.8 1
< 0.1%
36.9 1
< 0.1%
35.7 1
< 0.1%
34.7 1
< 0.1%
33.5 1
< 0.1%
29.6 1
< 0.1%
29.5 1
< 0.1%
29.3 1
< 0.1%

histogram_width
Real number (ℝ)

High correlation 

Distinct154
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.445908
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:44.601062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q137
median67.5
Q3100
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)63

Descriptive statistics

Standard deviation38.955693
Coefficient of variation (CV)0.55298731
Kurtosis-0.90228678
Mean70.445908
Median Absolute Deviation (MAD)31.5
Skewness0.31423475
Sum149768
Variance1517.546
MonotonicityNot monotonic
2025-03-08T11:14:45.006190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 42
 
2.0%
102 35
 
1.6%
27 30
 
1.4%
31 29
 
1.4%
90 28
 
1.3%
98 28
 
1.3%
96 27
 
1.3%
83 27
 
1.3%
22 27
 
1.3%
42 26
 
1.2%
Other values (144) 1827
85.9%
ValueCountFrequency (%)
3 2
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 10
0.5%
9 6
 
0.3%
10 9
0.4%
11 10
0.5%
12 20
0.9%
13 13
0.6%
ValueCountFrequency (%)
180 1
 
< 0.1%
176 6
0.3%
163 2
 
0.1%
162 1
 
< 0.1%
161 5
0.2%
158 2
 
0.1%
153 3
 
0.1%
150 10
0.5%
149 11
0.5%
148 8
0.4%

histogram_min
Real number (ℝ)

High correlation 

Distinct109
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.579492
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:45.433597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.560212
Coefficient of variation (CV)0.31588344
Kurtosis-1.2904222
Mean93.579492
Median Absolute Deviation (MAD)27
Skewness0.11578402
Sum198950
Variance873.80615
MonotonicityNot monotonic
2025-03-08T11:14:45.884961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 77
 
3.6%
52 50
 
2.4%
71 49
 
2.3%
120 48
 
2.3%
60 45
 
2.1%
68 43
 
2.0%
67 41
 
1.9%
103 40
 
1.9%
51 36
 
1.7%
62 35
 
1.6%
Other values (99) 1662
78.2%
ValueCountFrequency (%)
50 77
3.6%
51 36
1.7%
52 50
2.4%
53 32
1.5%
54 27
 
1.3%
55 20
 
0.9%
56 19
 
0.9%
57 22
 
1.0%
58 22
 
1.0%
59 17
 
0.8%
ValueCountFrequency (%)
159 1
 
< 0.1%
158 1
 
< 0.1%
156 1
 
< 0.1%
155 2
 
0.1%
154 3
 
0.1%
153 8
0.4%
152 4
0.2%
151 4
0.2%
150 3
 
0.1%
149 2
 
0.1%

histogram_max
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0254
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:46.278882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.944183
Coefficient of variation (CV)0.10939881
Kurtosis0.63276948
Mean164.0254
Median Absolute Deviation (MAD)11
Skewness0.57786245
Sum348718
Variance321.99371
MonotonicityNot monotonic
2025-03-08T11:14:47.107493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 71
 
3.3%
171 66
 
3.1%
158 62
 
2.9%
156 60
 
2.8%
159 58
 
2.7%
152 54
 
2.5%
154 52
 
2.4%
178 52
 
2.4%
172 48
 
2.3%
153 48
 
2.3%
Other values (76) 1555
73.1%
ValueCountFrequency (%)
122 2
 
0.1%
123 2
 
0.1%
125 3
 
0.1%
126 5
0.2%
127 2
 
0.1%
128 4
 
0.2%
129 10
0.5%
130 8
0.4%
131 7
0.3%
132 4
 
0.2%
ValueCountFrequency (%)
238 6
 
0.3%
230 3
 
0.1%
228 5
 
0.2%
213 1
 
< 0.1%
211 5
 
0.2%
210 4
 
0.2%
205 1
 
< 0.1%
204 3
 
0.1%
200 31
1.5%
199 20
0.9%

histogram_number_of_peaks
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0682032
Minimum0
Maximum18
Zeros107
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:47.438818image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9493856
Coefficient of variation (CV)0.72498483
Kurtosis0.50421053
Mean4.0682032
Median Absolute Deviation (MAD)2
Skewness0.89288591
Sum8649
Variance8.6988755
MonotonicityNot monotonic
2025-03-08T11:14:48.067723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
0 107
 
5.0%
8 106
 
5.0%
9 67
 
3.2%
Other values (8) 118
 
5.6%
ValueCountFrequency (%)
0 107
 
5.0%
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
8 106
 
5.0%
9 67
 
3.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
0.1%
15 1
 
< 0.1%
14 5
 
0.2%
13 10
 
0.5%
12 22
 
1.0%
11 28
 
1.3%
10 49
2.3%
9 67
3.2%
8 106
5.0%

histogram_number_of_zeroes
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32361242
Minimum0
Maximum10
Zeros1624
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:48.403580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70605937
Coefficient of variation (CV)2.1818056
Kurtosis30.365084
Mean0.32361242
Median Absolute Deviation (MAD)0
Skewness3.9202874
Sum688
Variance0.49851984
MonotonicityNot monotonic
2025-03-08T11:14:48.706474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
5 2
 
0.1%
4 2
 
0.1%
3 21
 
1.0%
2 108
 
5.1%
1 366
 
17.2%
0 1624
76.4%

histogram_mode
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.45202
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:49.070365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111.25
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.381289
Coefficient of variation (CV)0.11917823
Kurtosis3.0095305
Mean137.45202
Median Absolute Deviation (MAD)10
Skewness-0.99517784
Sum292223
Variance268.34664
MonotonicityNot monotonic
2025-03-08T11:14:49.505294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 140
 
6.6%
136 89
 
4.2%
150 89
 
4.2%
142 87
 
4.1%
148 79
 
3.7%
144 78
 
3.7%
129 76
 
3.6%
143 71
 
3.3%
125 67
 
3.2%
126 66
 
3.1%
Other values (78) 1284
60.4%
ValueCountFrequency (%)
60 6
0.3%
67 5
0.2%
69 1
 
< 0.1%
71 1
 
< 0.1%
75 6
0.3%
76 1
 
< 0.1%
77 1
 
< 0.1%
86 11
0.5%
88 6
0.3%
89 3
 
0.1%
ValueCountFrequency (%)
187 1
 
< 0.1%
186 6
0.3%
180 4
 
0.2%
179 1
 
< 0.1%
176 6
0.3%
170 4
 
0.2%
169 3
 
0.1%
167 8
0.4%
165 10
0.5%
164 1
 
< 0.1%

histogram_mean
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.61054
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:49.935862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.593596
Coefficient of variation (CV)0.11584232
Kurtosis0.93342749
Mean134.61054
Median Absolute Deviation (MAD)10
Skewness-0.65101924
Sum286182
Variance243.16025
MonotonicityNot monotonic
2025-03-08T11:14:50.408509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 65
 
3.1%
144 64
 
3.0%
135 63
 
3.0%
141 61
 
2.9%
140 60
 
2.8%
132 59
 
2.8%
133 58
 
2.7%
145 58
 
2.7%
136 57
 
2.7%
147 57
 
2.7%
Other values (93) 1524
71.7%
ValueCountFrequency (%)
73 1
 
< 0.1%
75 1
 
< 0.1%
76 1
 
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
80 2
0.1%
81 1
 
< 0.1%
82 2
0.1%
83 4
0.2%
84 3
0.1%
ValueCountFrequency (%)
182 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
175 1
< 0.1%
173 2
0.1%
172 1
< 0.1%
171 2
0.1%
170 1
< 0.1%
169 1
< 0.1%
168 1
< 0.1%

histogram_median
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.09031
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:51.223500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median139
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.466589
Coefficient of variation (CV)0.1047618
Kurtosis0.66725933
Mean138.09031
Median Absolute Deviation (MAD)10
Skewness-0.4784142
Sum293580
Variance209.28219
MonotonicityNot monotonic
2025-03-08T11:14:51.591395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 69
 
3.2%
137 68
 
3.2%
142 68
 
3.2%
145 67
 
3.2%
147 65
 
3.1%
151 63
 
3.0%
141 63
 
3.0%
134 62
 
2.9%
149 60
 
2.8%
143 56
 
2.6%
Other values (85) 1485
69.8%
ValueCountFrequency (%)
77 1
< 0.1%
78 1
< 0.1%
79 2
0.1%
82 1
< 0.1%
86 1
< 0.1%
87 1
< 0.1%
90 1
< 0.1%
91 1
< 0.1%
92 2
0.1%
93 1
< 0.1%
ValueCountFrequency (%)
186 1
< 0.1%
183 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 2
0.1%
174 2
0.1%
172 1
< 0.1%
171 1
< 0.1%
170 2
0.1%

histogram_variance
Real number (ℝ)

High correlation  Zeros 

Distinct133
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.80809
Minimum0
Maximum269
Zeros187
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-03-08T11:14:52.146003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile76
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation28.977636
Coefficient of variation (CV)1.5407006
Kurtosis15.131589
Mean18.80809
Median Absolute Deviation (MAD)6
Skewness3.2199738
Sum39986
Variance839.70339
MonotonicityNot monotonic
2025-03-08T11:14:52.736434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 248
 
11.7%
0 187
 
8.8%
2 166
 
7.8%
3 161
 
7.6%
4 108
 
5.1%
5 85
 
4.0%
8 74
 
3.5%
6 65
 
3.1%
7 53
 
2.5%
9 49
 
2.3%
Other values (123) 930
43.7%
ValueCountFrequency (%)
0 187
8.8%
1 248
11.7%
2 166
7.8%
3 161
7.6%
4 108
5.1%
5 85
 
4.0%
6 65
 
3.1%
7 53
 
2.5%
8 74
 
3.5%
9 49
 
2.3%
ValueCountFrequency (%)
269 1
< 0.1%
254 1
< 0.1%
250 1
< 0.1%
243 1
< 0.1%
241 1
< 0.1%
215 1
< 0.1%
195 1
< 0.1%
190 1
< 0.1%
182 1
< 0.1%
177 1
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.9 KiB
0.0
1115 
1.0
846 
-1.0
165 

Length

Max length4
Median length3
Mean length3.0776105
Min length3

Characters and Unicode

Total characters6543
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1115
52.4%
1.0 846
39.8%
-1.0 165
 
7.8%

Length

2025-03-08T11:14:53.074938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-08T11:14:53.391046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1115
52.4%
1.0 1011
47.6%

Most occurring characters

ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

fetal_health
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.7 KiB
1.0
1655 
2.0
295 
3.0
176 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Length

2025-03-08T11:14:53.697360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-08T11:14:54.051980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Most occurring characters

ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Interactions

2025-03-08T11:14:26.836022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:41.071499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:47.105262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:53.000604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:58.520824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:04.471891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:10.554592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:16.703504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:22.623996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:28.666586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:34.449478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:40.340558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:46.145577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:51.784365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:57.654956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:03.754689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:09.422769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:15.373952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:21.096416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:27.101993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:41.433099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:47.424206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:53.267944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:58.816964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:04.934844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:10.897673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:17.001294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:22.913921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:28.942792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:34.732831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:40.633405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:46.422173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:52.184400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:57.950513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:04.038143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:09.714614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:15.679212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:21.371222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:27.409526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:41.821888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:47.765846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:53.567062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:59.140575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:05.241420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:11.224813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:17.318197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:23.220669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:29.257403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:35.041498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:52.502062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:58.285682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:04.342033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:10.024728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:15.991388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:21.675899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:27.685471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:42.101946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:48.078849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:53.985786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:59.433435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:05.536680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:11.535033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:17.617109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:23.517889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:29.526239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:35.321179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:41.234518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:52.787261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:14:04.621401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:11.835114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:23.834919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:14:05.074061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:10.609228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:16.566520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:14:29.742242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:44.287263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:50.250531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:12:55.972317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:07.671438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:13.869026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:19.785617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:25.653822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:31.751709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:37.391329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:43.466478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:49.239541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:55.070374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:00.969394image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:06.852314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:14:07.437286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:32.720806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:38.470141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:55.925903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:14:07.724072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:13.521920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:19.418759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:12:51.434486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:12:57.406885image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:03.294902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:15.341790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:03.888012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-08T11:13:51.511497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:13:57.373835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:03.468820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:09.149221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:15.090760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:20.817794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-08T11:14:26.556324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-08T11:14:54.434420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
abnormal_short_term_variabilityaccelerationsbaseline valuefetal_healthfetal_movementhistogram_maxhistogram_meanhistogram_medianhistogram_minhistogram_modehistogram_number_of_peakshistogram_number_of_zeroeshistogram_tendencyhistogram_variancehistogram_widthlight_decelerationsmean_value_of_long_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilityprolongued_decelerationssevere_decelerationsuterine_contractions
abnormal_short_term_variability1.000-0.3440.3170.4530.260-0.1260.1370.1640.2690.126-0.166-0.1740.157-0.345-0.279-0.153-0.338-0.5210.4250.0300.114-0.218
accelerations-0.3441.000-0.1130.3140.0520.4700.2100.209-0.1670.1870.2380.0010.0990.3910.352-0.012-0.1020.327-0.491-0.1160.0000.120
baseline value0.317-0.1131.0000.295-0.0220.3260.7880.8410.3580.818-0.121-0.0650.275-0.239-0.155-0.174-0.055-0.3670.340-0.1410.086-0.134
fetal_health0.4530.3140.2951.0000.1650.1430.4960.4340.3130.4330.1240.0470.1690.2670.2920.1950.2200.3330.4790.4130.1590.238
fetal_movement0.2600.052-0.0220.1651.0000.116-0.080-0.041-0.180-0.0350.193-0.0930.0970.1150.1880.035-0.1140.071-0.0750.1230.000-0.310
histogram_max-0.1260.4700.3260.1430.1161.0000.3420.413-0.2920.4120.5020.1680.1700.5270.6490.233-0.0460.372-0.3210.1050.0500.113
histogram_mean0.1370.2100.7880.496-0.0800.3421.0000.9580.4710.908-0.191-0.1160.306-0.325-0.234-0.4700.077-0.4600.292-0.4050.404-0.171
histogram_median0.1640.2090.8410.434-0.0410.4130.9581.0000.3870.961-0.106-0.0740.355-0.216-0.135-0.3300.011-0.3590.245-0.3580.468-0.135
histogram_min0.269-0.1670.3580.313-0.180-0.2920.4710.3871.0000.346-0.700-0.3270.214-0.751-0.905-0.602-0.081-0.6950.465-0.3100.083-0.104
histogram_mode0.1260.1870.8180.433-0.0350.4120.9080.9610.3461.000-0.072-0.0730.370-0.152-0.103-0.2520.002-0.3130.222-0.3110.529-0.089
histogram_number_of_peaks-0.1660.238-0.1210.1240.1930.502-0.191-0.106-0.700-0.0721.0000.2930.1420.6470.7750.482-0.0020.552-0.3590.2410.0000.118
histogram_number_of_zeroes-0.1740.001-0.0650.047-0.0930.168-0.116-0.074-0.327-0.0730.2931.0000.0400.3020.3320.2760.0720.296-0.1650.0800.0000.061
histogram_tendency0.1570.0990.2750.1690.0970.1700.3060.3550.2140.3700.1420.0401.0000.1450.2460.1000.1010.0560.0270.2040.1350.087
histogram_variance-0.3450.391-0.2390.2670.1150.527-0.325-0.216-0.751-0.1520.6470.3020.1451.0000.8300.713-0.0930.779-0.6500.3800.2300.277
histogram_width-0.2790.352-0.1550.2920.1880.649-0.234-0.135-0.905-0.1030.7750.3320.2460.8301.0000.5840.0480.714-0.5260.2870.0970.139
light_decelerations-0.153-0.012-0.1740.1950.0350.233-0.470-0.330-0.602-0.2520.4820.2760.1000.7130.5841.000-0.2500.617-0.3850.3270.2360.303
mean_value_of_long_term_variability-0.338-0.102-0.0550.220-0.114-0.0460.0770.011-0.0810.002-0.0020.0720.101-0.0930.048-0.2501.000-0.020-0.043-0.2380.000-0.067
mean_value_of_short_term_variability-0.5210.327-0.3670.3330.0710.372-0.460-0.359-0.695-0.3130.5520.2960.0560.7790.7140.617-0.0201.000-0.6850.3010.0480.327
percentage_of_time_with_abnormal_long_term_variability0.425-0.4910.3400.479-0.075-0.3210.2920.2450.4650.222-0.359-0.1650.027-0.650-0.526-0.385-0.043-0.6851.000-0.2190.000-0.283
prolongued_decelerations0.030-0.116-0.1410.4130.1230.105-0.405-0.358-0.310-0.3110.2410.0800.2040.3800.2870.327-0.2380.301-0.2191.0000.0660.126
severe_decelerations0.1140.0000.0860.1590.0000.0500.4040.4680.0830.5290.0000.0000.1350.2300.0970.2360.0000.0480.0000.0661.0000.000
uterine_contractions-0.2180.120-0.1340.238-0.3100.113-0.171-0.135-0.104-0.0890.1180.0610.0870.2770.1390.303-0.0670.327-0.2830.1260.0001.000

Missing values

2025-03-08T11:14:32.858388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-08T11:14:34.001828image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
0120.00.0000.00.0000.0000.00.00073.00.543.02.464.062.0126.02.00.0120.0137.0121.073.01.02.0
1132.00.0060.00.0060.0030.00.00017.02.10.010.4130.068.0198.06.01.0141.0136.0140.012.00.01.0
2133.00.0030.00.0080.0030.00.00016.02.10.013.4130.068.0198.05.01.0141.0135.0138.013.00.01.0
3134.00.0030.00.0080.0030.00.00016.02.40.023.0117.053.0170.011.00.0137.0134.0137.013.01.01.0
4132.00.0070.00.0080.0000.00.00016.02.40.019.9117.053.0170.09.00.0137.0136.0138.011.01.01.0
5134.00.0010.00.0100.0090.00.00226.05.90.00.0150.050.0200.05.03.076.0107.0107.0170.00.03.0
6134.00.0010.00.0130.0080.00.00329.06.30.00.0150.050.0200.06.03.071.0107.0106.0215.00.03.0
7122.00.0000.00.0000.0000.00.00083.00.56.015.668.062.0130.00.00.0122.0122.0123.03.01.03.0
8122.00.0000.00.0020.0000.00.00084.00.55.013.668.062.0130.00.00.0122.0122.0123.03.01.03.0
9122.00.0000.00.0030.0000.00.00086.00.36.010.668.062.0130.01.00.0122.0122.0123.01.01.03.0
baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
2116140.00.0040.0000.0040.0000.00.080.00.236.02.218.0140.0158.01.00.0147.0148.0149.01.00.01.0
2117140.00.0000.0000.0080.0000.00.079.00.320.08.526.0124.0150.01.00.0144.0143.0145.01.01.01.0
2118140.00.0000.0000.0060.0010.00.079.00.526.07.021.0129.0150.01.00.0145.0142.0145.02.01.01.0
2119140.00.0000.0000.0070.0010.00.079.00.627.06.426.0124.0150.01.00.0144.0141.0145.01.01.01.0
2120140.00.0000.0000.0050.0010.00.077.00.717.06.031.0124.0155.02.00.0145.0143.0145.02.00.01.0
2121140.00.0000.0000.0070.0000.00.079.00.225.07.240.0137.0177.04.00.0153.0150.0152.02.00.02.0
2122140.00.0010.0000.0070.0000.00.078.00.422.07.166.0103.0169.06.00.0152.0148.0151.03.01.02.0
2123140.00.0010.0000.0070.0000.00.079.00.420.06.167.0103.0170.05.00.0153.0148.0152.04.01.02.0
2124140.00.0010.0000.0060.0000.00.078.00.427.07.066.0103.0169.06.00.0152.0147.0151.04.01.02.0
2125142.00.0020.0020.0080.0000.00.074.00.436.05.042.0117.0159.02.01.0145.0143.0145.01.00.01.0

Duplicate rows

Most frequently occurring

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health# duplicates
0122.00.0000.0000.0000.00.00.019.01.90.015.139.0103.0142.01.00.0120.0120.0122.03.00.01.04
1123.00.0000.0000.0000.00.00.049.00.87.013.874.063.0137.02.00.0129.0127.0129.02.01.01.02
2123.00.0030.0030.0000.00.00.052.00.82.015.490.050.0140.07.00.0129.0128.0130.04.01.01.02
3123.00.0030.0040.0000.00.00.050.00.94.014.882.058.0140.07.00.0129.0128.0130.05.01.01.02
4135.00.0000.0000.0000.00.00.062.00.571.06.997.071.0168.03.00.0143.0142.0144.01.01.03.02
5138.00.0020.0000.0040.00.00.041.00.88.010.351.0105.0156.04.00.0142.0142.0143.02.01.01.02
6140.00.0070.0000.0040.00.00.034.01.20.010.360.0119.0179.02.00.0156.0153.0155.05.00.01.02
7144.00.0000.0190.0000.00.00.076.00.461.010.681.071.0152.03.00.0145.0144.0146.02.01.02.02
8145.00.0000.0200.0000.00.00.077.00.245.05.821.0129.0150.01.00.0146.0145.0147.00.01.02.02
9146.00.0000.0000.0030.00.00.065.00.439.07.019.0137.0156.01.00.0150.0149.0151.01.01.02.02